Tracking with general regression

A method is developed to track planar and near-planar objects by incorporating a model of the expected image template distortion, and fitting the sampling region to pre-trained examples with general regression. The approach does not assume a particular form of the underlying space, allows a natural handling of occluding objects, and permits dynamic changes of the scale and size of the sampled region. The implementation of the algorithm runs comfortably in modest hardware at video-rate. Research supported by Grants GR/N03266 and GR/S97774 from the UK Engineering and Physical Science Research Council, and by a Mexican CONACYT scholarship to WWM.